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arXiv · 2509.17000

Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals

Shuhao Jiang·Songbo Wang·Yang Qiao·Chun Xu·Chaoyang Zheng·Shengyi Zhou·Huanjun Wang·Fangming Li·Cong Zhang·Jiyu Wang
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Abstract

Large Reasoning Models (LRMs) often suffer from computational inefficiency due to overthinking, where a fixed reasoning budget fails to match the varying complexity of tasks. To address this issue, we propose Adaptive Overclocking, a method that makes the overclocking hyperparameter $α$ dynamic and context-aware. Our method adjusts reasoning speed in real time through two complementary signals: (1) token-level model uncertainty for fine-grained step-wise control, and (2) input complexity estimation for informed initialization. We implement this approach with three strategies: Uncertainty-Aware Alpha Scheduling (UA-$α$S), Complexity-Guided Alpha Initialization (CG-$α$I), and a Hybrid Adaptive Control (HAC) that combines both. Experiments on GSM8K, MATH, and SVAMP show that HAC achieves superior accuracy-latency trade-offs, reducing unnecessary computation on simple problems while allocating more resources to challenging ones. By mitigating overthinking, Adaptive Overclocking enhances both efficiency and overall reasoning performance.

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Shuhao Jiang, Songbo Wang, Yang Qiao, Chun Xu, Chaoyang Zheng, Shengyi Zhou, Huanjun Wang, Fangming Li, Cong Zhang, Jiyu Wang. 2025-09-21. Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals. https://arxiv.org/abs/2509.17000

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